It has been a longstanding goal of AI and robotics to build autonomous vehicles that can move around on land, in the sea, and in the air interacting with the physical world to achieve their goals. In recent years, the increasing availability of capable mobile platforms, manipulators, and high-precision sensors, coupled with advances in perception, localization and planning algorithms have brought us much closer to achieving this goal.

Robotic platforms have demonstrated autonomous navigation in large complex spaces for prolonged periods of time while robotic manipulators have demonstrated autonomous manipulation of objects in cluttered spaces. However, effective, task-oriented motion inevitably requires a principled approach to integrating task planning and motion planning that is capable of operating in real-time in dynamic and complex environments. Historically, general but discrete task planning has been considered extensively in the AI community while specialized continuous motion planning has been the focus in robotics.

The goal of this workshop was to investigate principled approaches to bridging the gap between these two levels of planning, to foster the exchange of ideas between the two communities of researchers, and to work towards developing common benchmarks and an infrastructure for evaluating the approaches to this problem.